LO29: ILearnEM.com: a curation of quality FOAM resources to learn the fundamentals of emergency medicine
Bibliographic record
Abstract
Introduction/Innovation Concept: Free Open Access Medical Education (FOAM) is an emerging movement enabling crowdsourced sharing of vast amounts of medical knowledge on the web, especially in the dynamic field of emergency medicine (EM). However, the wide range of FOAM producers and the lack of organization in published FOAM content results in a challenge for learners to find quality resources that meet their educational needs. ILearnEM addresses this by curating content from popular FOAM sites to provide both new and seasoned learners with an organized, topic-structured EM curriculum. Methods: The resources on ILearnEM.com are drawn from the top 50 scoring websites on the Social Media Index (SMI), an indirect measure of quality and impact for online educational resources. The quality of each individual resource is reviewed by our curators using published Quality Checklists developed specifically for FOAM. Links to the original resources are systematically organized into core EM topics and separated into “Approach to” and “Beyond the Basics” categories. Curriculum, Tool, or Material: Since its launch in February 2016, ILearnEM.com has been distributed to the University of Ottawa medical students and residents, the Canadian CCFP-EM program directors, and through social media. Content on the website is updated every two weeks by our curators through an analysis of recent online publications from each of the top 50 SMI sites. The new resources are selected based on the level of quality and the relevance to the fundamentals of EM. Content updates are announced on social media (Twitter) to further engage learners by identifying the availability of new material. Conclusion: Based on a 10-month traffic analysis, 4234 unique visitors visited ILearnEM.com with an average of 1.9 visits/person and 10.4 pages/visit. Of those responding to an online survey (n=138, response rate=3.3%) visitors were 42.8% (n=59) residents, 29.0% medical students (n=40), 19.6% practicing physicians (n=27), and 8.7% other healthcare professionals (n=12). As one of few sites with an objective for a learner-oriented approach to curating content, ILearnEM will continue to be updated regularly based on user feedback to benefit the fast growing consumer base of medical student and resident learners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.090 | 0.055 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".